A Unified Detection Framework for Ransomware Using Safeguarded Diffusion Models

Abstract Ransomware continues to pose an evolving threat to digital infrastructures, necessitating the development of advanced detection mechanisms capable of addressing both known and novel attack vectors. This research explores the application of diffusion models—traditionally used for generative tasks—for detecting ransomware attacks through anomaly detection. The proposed framework integrates safeguarding techniques to protect the diffusion model against adversarial threats, ensuring robust detection of ransomware even in dynamic and large-scale network environments. Experimental results demonstrate that the diffusion model outperforms traditional signature-based and anomaly-based methods in terms of accuracy, false-positive rates, and detection latency, while maintaining efficiency across different network scales. The framework's effectiveness in real-time ransomware detection, coupled with its scalability, highlights its potential for deployment in diverse cybersecurity environments. Despite its successes, challenges such as distinguishing between benign anomalies and ransomware behaviour and optimising scalability remain. This research contributes to advancing the use of machine learning in ransomware detection and sets the stage for future exploration of adaptive security frameworks.